The next subsections describe the EFA performed in the study. EFA was utilised to assess the construct validity of the following constructs: System Features and Internal Differences measured as per the study’s conceptual model (Table 4.18). As mentioned earlier, only these two constructs are scrutinised as it was believed that they are the most relevant in the context of the present study.
Table 4.17 Constructs Included in the Conceptual Framework
Construct Sub-Construct
Individual Differences
Computer Self-efficacy (SE) Computer Experience (CS) Domain Knowledge (DK) Motivation (M0)
System Features
Accessibility (AC) Visibility (VI) Relevance (RE)
4.7.2.1 EFA for System Features (Accessibility, Visibility, Relevance)
The Kaiser-Meyer-Olkin (KMO) test and the Bartlett’s test of sphericity are typically utilised to ascertain the factorability of the output matrix of a scale (Coakes, 2005; Pallant, 2005). In general, the feasibility of the factor analysis is indicated by high values of the KMO test (>0.50; de Vaus, 2002; Field, 2005; Netemeyer, Bearden, & Sharma, 2003) and high significance value of the Bartlett’s test. The KMO Measure of Sampling Adequacy, with a value of 0.866, indicates that the sample size was sufficiently large to perform factor analysis for the System Features construct.
Moreover, the Bartlett’s test of sphericity was significant with p=0.000, indicating adequate correlations between the variables (Table 4.19).
Table 4.18 KMO and Bartlett's Test for System Features
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.866
Bartlett's Test of Sphericity
Approx. Chi-Square 4031.604
df 78
Sig. 0.000
189 The outcomes of the factor analysis for the System Features construct are provided in Table 4.20.
Factors with eigenvalues of >1 and a factor loading of at least 0.5 were considered acceptable. It can be seen that the facets related to the Accessibility of a system was the most important factor that could explain 49.697% of the variance in system features, followed by Visibility and Relevance. Moreover, it could be seen that all the items in each construct had factor values greater than the cut-off level.
Table 4.19 Factors of System Features Variable
Code Factors Factor
loadings
% of Variance
Cumulative
%
Accessibility 49.697 49.697
AC1 I find it easy to navigate 0.846
AC2 I am able to use it whenever I need it 0.828
AC3 I find it easy to get access to 0.859
AC4 It is easily accessible 0.773
AC5 I can locate the resources I need 0.848
Visibility 16.385 66.082
VI1 People at my university know that it exists 0.869 VI2 People know where to look to find it 0.855
VI3 I find that it is always available 0.740
Relevance 8.371 74.453
RE1 It has resources that relate to my area of interest 0.739 RE2 It has enough resources for my study 0.845 RE3 It provides current information in my area of
interest 0.540
RE4 It is a very efficient study tool 0.510
RE5 It is limited in its coverage of my area of interest 0.886
The EFA for the UDL dataset is described next.
UDL Dataset
The KMO Measure of Sampling Adequacy, with a value of 0.753, indicates that the sample size was sufficiently large to perform factor analysis for the System Features construct in the UDL
190 dataset. Moreover, the Bartlett’s test of sphericity was significant with p=0.000, indicating adequate correlations between the variables (Table 4.21).
Table 4.20 KMO and Bartlett’s test for System Features – UDL Dataset Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.753 Bartlett's Test of Sphericity Approx. Chi-Square 1411.543
df 78
Sig. 0.000
The outcomes of the factor analysis for the System Features construct are provided in Table 4.22.
Factors with eigenvalues of >1 and a factor loading of at least 0.5 were considered acceptable. In contrast to the combined dataset, it can be seen that the facets related to the Relevance of a system was the most important factor that could explain 38.003% of the variance in system features, followed by Accessibility and Visibility. Further, it can be seen that all the items in each construct had factor values greater than the cut-off level.
Table 4.21 Factors of System Features – UDL Dataset Variable
Code Factors Factor Loadings % of
Variance
Cumulative
%
Relevance 38.003 38.003
RE1 It has resources that relate to my area
of interest 0.778
RE2 It has enough resources for my study 0.827
RE3 It provides current information in my
area of interest 0.675
RE4 It is a very efficient study tool 0.511
RE5 It is limited in its coverage of my area
of interest 0.875
Accessibility 15.526 53.530
AC1 I find it easy to navigate 0.817
191 Variable
Code Factors Factor Loadings % of
Variance
Cumulative
%
AC2 I am able to use it whenever I need it 0.732
AC3 I find it easy to get access to 0.864
AC4 It is easily accessible 0.711
AC5 I can locate the resources I need 0.814
Visibility 13.414 66.944
VI1 People at my university know that it
exists 0.871
VI2 People know where to look to find it 0.900
VI3 I find that it is always available 0.709
Google Scholar Dataset
The KMO Measure of Sampling Adequacy, with a value of 0.800, indicates that the sample size was sufficiently large to perform factor analysis for the System Features construct in the Google Scholar dataset. Moreover, the Bartlett’s test of sphericity was significant with p=0.000, indicating adequate correlations between the variables (Table 4.23).
Table 4.22 KMO and Bartlett’s Test for System Features - Google Scholar Dataset Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.800 Bartlett's Test of Sphericity Approx. Chi-Square 1383.825
df 55
Sig. 0.000
The outcomes of the factor analysis for the System Features construct are provided in Table 4.24.
Factors with eigenvalues of >1 and a factor loading of at least 0.5 were considered acceptable.
Similar to the UDL dataset, it can be seen that the facets related to the Relevance of a system was the most important factor that could explain 45.619% of the variance in system features, followed by Accessibility and Relevance. Moreover, it can be seen that all the items in each construct had factor values greater than the cut-off level.
192 Table 4.23 Factors of System Features - Google Scholar Dataset
Variable Code
Factors Factor Loadings % of
Variance
Cumulative
%
Relevance 45.619 45.619
RE1 It has resources that relate to my
area of interest 0.741
RE2 It has enough resources for my study 0.817 RE5 It is limited in its coverage of my
area of interest 0.858
Accessibility 17.741 63.360
AC1 I find it easy to navigate 0.891
AC2 I am able to use it whenever I need
it 0.795
AC3 I find it easy to get access to 0.866
AC4 It is easily accessible 0.727
AC5 I can locate the resources I need 0.833
Visibility 10.881 74.241
VI1 People at my university know that it
exists 0.877
VI2 People know where to look to find it 0.823 VI3 I find that it is always available 0.829
4.7.2.2 EFA for Internal Differences (Domain Knowledge, Computer Experience, Computer Self-efficacy, Motivation)
The KMO Measure of Sampling Adequacy, with a value of 0.791, indicates that the sample size was sufficiently large to perform factor analysis for the Internal Differences construct. Moreover, the Bartlett’s test of sphericity was significant with p=0.000, indicating adequate correlations between the variables (Table 4.25).
Table 4.24 KMO and Bartlett’s Test for Internal Differences
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.791
Bartlett's Test of Sphericity
Approx. Chi-Square 4433.648
df 171
Sig. 0.000
193 The outcomes of the factor analysis for the Internal Differences construct are provided in Table 4.26. Factors with eigenvalues of >1 and a factor loading of at least 0.5 were considered acceptable.
It can be seen that the facets related to the Domain Knowledge of an individual was the most important factor that could explain 31.259% of the variance in internal differences, this was followed by Motivation, Computer Self-efficacy, and Computer Experience. Moreover, it could be seen that all the items in each construct had factor values greater than the cut-off level.
Table 4.25 Factors of Internal Differences
Variable Code Factors Factor loadings % of
Variance
Cumulative
%
Domain Knowledge 31.259 31.259
DK1 I am familiar with the subject
domain that I search for 0.880 DK2 I am knowledgeable in the topic to
search for 0.894
DK3 I have previous experience
searching in this subject domain 0.848 DK4
I have the domain knowledge that it necessary to search for what I want to find
0.840
Motivation 14.069 45.328
MO1 Helps me achieve in my studies 0.861 MO2 I use it because people around me do 0.726
MO3 I have been trained to use it 0.762
MO4 I am confident in using it 0.457
MO5 I don’t always feel in control of the
outcome 0.798
MO6 Makes me feel really involved in my
studies 0.456
Computer Self-Efficacy 11.198 56.526
SE1 I feel confident in my ability to use
it 0.794
SE2 I can use it even if there is no one
around me to show me 0.693
SE3 I don’t need a lot of time to complete
my task using it 0.767
SE4 I often find it difficult to use it for
my studies 0.659
194
Variable Code Factors Factor loadings % of
Variance
Cumulative
% SE5 Helps even when the task is
challenging 0.767
Computer Experience 8.334 64.860
CS1 I am confident in using computers 0.800 CS2 I think I am efficient in the use of a
computer to complete my task 0.900 CS3 I can use a computer even if there is
no one around to show me 0.872 CS4 I am happier if there is someone
around to ask for help 0.431 UDL Dataset
The KMO Measure of Sampling Adequacy, with a value of 0.675, indicates that the sample size was sufficiently large to perform factor analysis for the Internal Differences construct in the UDL dataset. Moreover, the Bartlett’s test of sphericity was significant with p=0.000, indicating adequate correlations between the variables (Table 4.27).
Table 4.26 KMO and Bartlett’s test for Individual Differences – UDL Dataset Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.675 Bartlett's Test of Sphericity 1639.049 1364.857
136 153
0.000 0.000
The outcomes of the factor analysis for the Internal Differences construct are provided in Table 4.28. Factors with eigenvalues of >1 and a factor loading of at least 0.5 were considered acceptable.
It could be seen that the facets related to the Domain Knowledge of an individual was the most important factor that could explain 22.222% of the variance in internal differences, this was followed by Computer Experience, Motivation, and Computer Self-efficacy. In contrast to the combined dataset, the factor loadings of items CS4 and MO6 did not meet the cut-off and could be excluded from further analysis.
195 Table 4.27 Factors of Individual Differences – UDL Dataset
Variable Code Factors
Factor Loadin gs
% of Varian ce
Cumul ative %
Domain Knowledge 22.222 22.222
DK1 I am familiar with the subject domain that I search
for 0.800
DK2 I am knowledgeable in the topic to search for 0.801 DK3 I have previous experience searching in this subject
domain 0.760
DK4 I have the domain knowledge that it necessary to
search for what I want to find 0.720
Computer Experience 16.897 39.120
CS1 I am confident in using computers 0.826
CS2 I think I am efficient in the use of a computer to
complete my task 0.956
CS3 I can use a computer even if there is no one around
to show me 0.926
Motivation 13.501 52.621
MO1 Helps me achieve in my studies 0.871
MO2 I use it because people around me do 0.649
MO3 I have been trained to use it 0.699
MO4 I am confident in using it 0.541
MO5 I don’t always feel in control of the outcome 0.813
Computer Self-Efficacy 10.178 62.799
SE1 I feel confident in my ability to use it 0.812
SE2 I can use it even if there is no one around me to
show me 0.711
SE3 I don’t need a lot of time to complete my task using
it 0.754
SE4 I often find it difficult to use it for my studies 0.657
SE5 Helps even when the task is challenging 0.719
Google Scholar Dataset
The KMO Measure of Sampling Adequacy, with a value of 0.669, indicates that the sample size was sufficiently large to perform factor analysis for the Internal Differences construct in the Google Scholar dataset. Moreover, the Bartlett’s test of sphericity was significant with p=0.000, indicating adequate correlations between the variables (Table 4.29).
196 Table 4.28 KMO and Bartlett’s test for Individual Differences – Google Scholar Dataset
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.669 Bartlett's Test of Sphericity Approx. Chi-Square 1364.857
df 153
Sig. 0.000
The outcomes of the factor analysis for the Internal Differences construct are provided in Table 4.30. Factors with eigenvalues of >1 and a factor loading of at least 0.5 were considered acceptable.
It could be seen that the facets related to the Domain Knowledge of an individual was the most important factor that could explain 21.297% of the variance in internal differences, this was followed by Computer Experience, Computer Self-efficacy, and Motivation. In contrast to the combined dataset, the factor loadings of item MO4 did not meet the cut-off and could be excluded from further analysis.
Table 4.29 Factors of Individual Differences – Google Scholar Dataset
Variable Code Factors Factor
Loadings
% of Variance
Cumul ative
%
Domain Knowledge 21.297 21.297
DK1 I am familiar with the subject domain that I search for
0.798
DK2 I am knowledgeable in the topic to search for 0.794
DK3 I have previous experience searching in this subject domain
0.774
DK4 I have the domain knowledge that it necessary
to search for what I want to find
0.715
Computer Experience 14.627 35.925
CS1 I am confident in using computers 0.689
CS2 I think I am efficient in the use of a computer to complete my task
0.839
CS3 I can use a computer even if there is no one around to show me
0.797
197
Variable Code Factors Factor
Loadings
% of Variance
Cumul ative
% CS4 I am happier if there is someone around to ask
for help
0.612
Computer Self-Efficacy 12.349 48.274
SE1 I feel confident in my ability to use it 0.787
SE2 I can use it even if there is no one around me to show me
0.681
SE3 I don’t need a lot of time to complete my task
using it
0.782
SE4 I often find it difficult to use it for my studies 0.618
SE5 Helps even when the task is challenging 0.741
Motivation 10.344 58.617
MO1 Helps me achieve in my studies 0.844
MO2 I use it because people around me do 0.752
MO3 I have been trained to use it 0.776
MO5 I don’t always feel in control of the outcome 0.800
MO6 Makes me feel really involved in my studies 0.526
The next section describes the CFA performed in the study in further detail.